Stage 4
Biowulf2
cd /data/OGVFB_BG/scEiaD/2024_02_28/snakeout/hs111_mature_eye_full/neural_cells
mamba deactivate; mamba activate; bash ~/git/scEiaD_modeling/Snakemake.wrapper.sh ~/git/scEiaD_modeling/workflow/Snakefile ~/git/scEiaD_modeling/config/config_hs111_mature_eye_full__neural.yaml ~/git/scEiaD_modeling/config/cluster.json
Assess Output
library(tidyverse)
source('analysis_scripts.R')
obs_neural <- pull_obs('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.obs.csv.gz', machine_label = 'MCT_scANVI_step4', drop_col = FALSE)
`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.`summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.
diff_neural <- pull_diff("~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.difftesting.leiden3.csv.gz")
'select()' returned 1:many mapping between keys and columns
Warning: Detected an unexpected many-to-many relationship between `x` and `y`.Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
Ratio (percentage) of labelled cell types for each leiden3
cluster
obs_neural$labels %>%
arrange(mCT) %>%
mutate(leiden3 = as.factor(leiden3)) %>%
DT::datatable(filter = 'top')
Mixed clusters
obs_neural$labels %>%
filter(grepl(",", mMCT)) %>%
arrange(mCT) %>%
mutate(leiden3 = as.factor(leiden3)) %>%
DT::datatable(filter = 'top')
UMAP Plots
obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = MCT_scANVI_step4), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(MCT_scANVI_step4) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = MCT_scANVI_step4, color = MCT_scANVI_step4)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = MCT_scANVI), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = studyRatio), pointsize = 0.7) +
scale_color_viridis_c() +
cowplot::theme_cowplot()

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = mMCT), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(mMCT) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = mMCT, color = mMCT)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none")

NA
NA
hclust
Take pseudobulk values (at the cluster level) and hierarchically
cluster them to ensure there aren’t any issues in either the overall
structure (e.g. rod and cones are intersperse)d and/or to identify any
potential mislabeled clusters
pb <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.pseudoBulk.leiden3.csv.gz')
colnames(pb) <- gsub("\\.\\d+","",colnames(pb))
hvg <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hvg5000.csv.gz')[-1,]
rnames <- pb$V1
clust <- str_extract(rnames, '\\d+') %>% as.integer()
pb <- pb[,-1] %>% as.matrix()
row.names(pb) <- as.character(clust)
pb <- pb[as.character(obs_neural$labels$leiden3),]
pb_norm <- metamoRph::normalize_data(t(pb), sample_scale = 'cpm') %>% t()
Sample CPM scaling
log1p scaling
# remove cell cycle genes
conv_table <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db,
keys=gsub('\\.\\d+','',unique(colnames(pb_norm))),
columns=c("ENSEMBL","SYMBOL", "MAP","GENENAME", "ENTREZID"), keytype="ENSEMBL")
'select()' returned 1:many mapping between keys and columns
cc_genes <- hvg %>% mutate(ENSEMBL = gsub("\\.\\d+","",V2)) %>%
left_join(conv_table, by = "ENSEMBL") %>%
mutate(cc_genes = case_when(SYMBOL %in% (Seurat::cc.genes.updated.2019 %>% unlist()) ~ TRUE)) %>%
filter(cc_genes) %>% pull(V2)
ribo_genes <- hvg %>% mutate(ENSEMBL = gsub("\\.\\d+","",V2)) %>%
left_join(conv_table, by = "ENSEMBL") %>% filter(grepl("^RPL|^RPS|^MT",SYMBOL)) %>%
pull(SYMBOL)
pb_norm <- pb_norm[,hvg$V2]
#pb_norm <- pb_norm[,hvg$V2[!hvg$V2 %in% c(cc_genes,ribo_genes)]]
# https://stats.stackexchange.com/questions/31565/compute-a-cosine-dissimilarity-matrix-in-r
sim <- pb_norm / sqrt(rowSums(pb_norm * pb_norm))
sim <- sim %*% t(sim)
D_sim <- as.dist(1 - sim)
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
hclust_sim <- hclust(D_sim, method = 'average')
hclust_sim$labels <- obs_neural$labels %>% pull(leiden3)
library(ggtree)
p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels, by = c("label" = "leiden3")) %>%
mutate(techRatio = round(techRatio, digits = ))
p + layout_dendrogram() +
geom_tiplab(aes(label = paste(label, mMCT, studyCount, TotalCount, techRatio, sep = ' - '), color = mCT)) +
theme_dendrogram(plot.margin=margin(16,16,300,16)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
guides(color="none")

p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels %>% mutate(studies = case_when(studyCount ==1 ~ studies,
TRUE ~ "multiple")), by = c("label" = "leiden3"))
p + layout_dendrogram() +
geom_tiplab(aes(label = paste(label, mMCT, studies, sep = ' - '), color = mCT)) +
geom_tippoint(aes(shape = studies), size= 3) +
theme_dendrogram(plot.margin=margin(16,16,300,16)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
guides(color="none")

p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels %>% mutate(studies = case_when(studyRatio ==1 ~ studiesRatio,
TRUE ~ "multiple")), by = c("label" = "leiden3"))
p + layout_dendrogram() +
geom_tiplab(aes(label = paste(label, mMCT, studies, sep = ' - '), color = mCT)) +
geom_tippoint(aes(shape = studies), size= 3) +
theme_dendrogram(plot.margin=margin(16,16,300,16)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
guides(color="none")

NA
NA
suspicious clusters
reasons:
- study specific clusters in “wrong” (not with like) parts of the
tree
- mixed cell types
sus_neural <- #c(71,93,126,112,144,39,21,85,135,7,74,36,124)
c(
# reason 1
c(99, 97, 38, 50,40, 98, 94),
# reason 2
c(85, 95, 91, 52, 77)
)
# to provide an additional layer of resolution to the cell type
hr_neural <- list()
UMAP Plots
obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = MCT_scANVI_step4), pointsize = 0.8, alpha = 0.5) +
scattermore::geom_scattermore(data = obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(leiden3 %in% sus_neural),
color = 'red', pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
filter(leiden3 %in% sus_neural) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot()

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural) %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = mCT), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
filter(leiden3 %in% sus_neural) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot()

Call CT
hr_neural <- list()
photoreceptor
Removing two more clusters with no ARR3 / RHO signal (and
suspicious hclust) above
https://www.nature.com/articles/s41598-020-66092-9
tib <- diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT %in% c('rod','cone'), !base %in% sus_neural) %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('ARR3','OPN1LW','OPN1SW','RHO', 'OPN1MW', 'RCVRN',"CRX","PROM1")) %>%
select(SYMBOL, base, logfoldchanges) %>%
pivot_wider(values_from = logfoldchanges, names_from = base)
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

sus_neural <- c(sus_neural,
c(86,29))
hr_neural$`cone (s)` <- c(87)
hr_neural$`cone (ml)` <- c(72,74,20,90,81)

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural,
mCT %in% c("rod", "cone")) %>%
left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
by = 'leiden3') %>%
mutate(`Cell Type` = case_when(is.na(`Cell Type`) ~ mCT,
TRUE ~ `Cell Type`)) %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values =
c(pals::alphabet2(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) +
cowplot::theme_cowplot()

bipolar

tib <- diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT %in% c('bipolar','rod bipolar'), !base %in% sus_neural) %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('PRKCA','GRM6','GRIK1','NIF3L1',
'LINC00470','DOK5','NELL2','STX18',
'ODF2L','FAM19A4','MEIS2','CALB1', 'FUT4',
'SCG2','LRPPRC','FEZF1')) %>%
select(SYMBOL, base, logfoldchanges) %>%
pivot_wider(values_from = logfoldchanges, names_from = base)
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

hr_neural$`bipolar (rod)` <- c(12,78,32)
hr_neural$`bipolar (off)` <- c(11,28,33,36,46,17)
hr_neural$`bipolar (on)` <- c(68,55,6,82,27,30,39)
obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural,
mCT %in% c("bipolar", "rod bipolar")) %>%
left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values =
c(pals::alphabet2(), pals::glasbey(),
pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) +
cowplot::theme_cowplot()

horizontal

diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT == 'horizontal') %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('ONECUT1','ONECUT2','LHX1','ISL1')) %>%
select(SYMBOL, base, logfoldchanges) %>%
ggplot(aes(x=as.factor(base), y=SYMBOL, color = logfoldchanges)) +
geom_point(size =10) +
scale_color_viridis_c()
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.

tib <- diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT == 'horizontal') %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('ONECUT1','ONECUT2','LHX1','ISL1')) %>%
select(SYMBOL, base, logfoldchanges) %>%
pivot_wider(values_from = logfoldchanges, names_from = base)
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

hr_neural$`horizontal (h1)` <- c(18,70)
hr_neural$`horizontal (h2)` <- c(58)
obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural,
mCT %in% c("horizontal")) %>%
left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values =
c(pals::alphabet2(), pals::glasbey(),
pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) +
cowplot::theme_cowplot()

amacrine
Most amacrine cells are inhibitory neurons utilizing GABA or glycine
as neurotransmitters. By assessing the expression of marker genes for
GABAergic (glutamate carboxylase, GAD1 and GAD2) and glycinergic
(SLC6A9, encoding the high affinity glycine transporter GLYT1)
amacrines20, we identified 16 putative GABAergic and 8 putative
glycinergic amacrine cell types among a total of 25 types (Fig. 3a,b).
One type (C14) expressed none of these three genes at high levels, and
might correspond to a non-GABAergic non-Glycinergic (nGnG) type
identified in mouse2122 . One of the glycinergic types (C17) also
expressed GAD2, raising the possibility that it uses both
transmitters.
REMOVING CLUSTER 0 AS IT IS STUDY SPECIFIC AND NOT EXPRESSING
ANY KNOWN GABA/GLYCI MARKERS
tib <- diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT == 'amacrine') %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('GAD1','GAD2','SLC6A9','NFIA')) %>%
select(SYMBOL, base, logfoldchanges) %>%
pivot_wider(values_from = logfoldchanges, names_from = base)
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

sus_neural <- c(sus_neural,
c(0))
hr_neural$`amacrine (gabanergic)` <- c(76,67,56,59,37,62,73,15,69,44,75,83,24,66,22,26,80,41,61,91,42)
hr_neural$`amacrine (glycinergic)` <- c(25,16,88,43)
hr_neural$`amacrine (gaba/glyci)` <- c(91,31,65)
obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural,
mCT %in% c("amacrine")) %>%
left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
by = 'leiden3') %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values =
c(pals::alphabet2(), pals::glasbey(),
pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) +
cowplot::theme_cowplot()

retinal ganglion
These don’t match up to the published markers very well - for example
the parasol RG seem to be a mixed ON/OFF cluster. Should run a retinal
ganglion specific scVI run and see whether that does a better job.
tib <- diff_neural$diff_testing %>%
left_join(obs_neural$labels, by = c('base'='leiden3')) %>%
filter(mCT %in% c('retinal ganglion'), !base %in% sus_neural) %>%
left_join(conv_table) %>%
filter(SYMBOL %in% c('TPBG','TBR1','FABP4','CHRNA2', 'LMO2',
'EOMES','SSTR2','FOXP2','FOXP1','PRR35','CARTPT',
'CDKN2A','ARPP21','OPN4', 'NEFM',
'TUBB3')) %>%
select(SYMBOL, base, scores) %>%
pivot_wider(values_from = scores, names_from = base)
Joining with `by = join_by(ENSEMBL)`Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-15, 0, 15), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(mCT == 'retinal ganglion', SubCellType != '') %>%
left_join(obs_neural$labels) %>%
group_by(leiden3, SubCellType) %>%
summarise(Count = n()) %>%
mutate(Ratio = Count/sum(Count)) %>%
filter(Ratio > 0.5) %>% arrange(SubCellType)
Joining with `by = join_by(leiden3, TotalCount, mMCT, mCount, mMCT_Ratio, mCT, aMCT, aCount, aMCT_Ratio, a_Ratio_sum, subCT, subCount, subCT_Ratio, subRatio_sum, studyCount, studies, studyRatio, studiesRatio, tissueCount, tissues, tissueRatio, techRatio)``summarise()` has grouped output by 'leiden3'. You can override using the `.groups` argument.
hr_neural$`retinal ganglion (ON midget)` <- c(45, 54,63, 92)
hr_neural$`retinal ganglion (OFF midget)` <- c(19,34,57,71,79, 89, 96)
hr_neural$`retinal ganglion (parasol)` <- c(64)

obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
filter(!leiden3 %in% sus_neural,
mCT %in% c("retinal ganglion")) %>%
left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
by = 'leiden3') %>%
mutate(`Cell Type` = case_when(is.na(`Cell Type`) ~ mCT,
TRUE ~ `Cell Type`)) %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 1) +
ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = leiden3)) +
scale_color_manual(values =
c(pals::glasbey(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) +
cowplot::theme_cowplot()

NEW
Update overall graphics on the new labels
UMAP
hr_long <- hr_neural %>% enframe(name = 'hrCT', value = 'leiden3') %>% unnest(leiden3)
obs_neural$nobs <- obs_neural$obs %>%
left_join(obs_neural$labels, by = 'leiden3') %>%
left_join(hr_long,
by = 'leiden3') %>%
filter(!leiden3 %in% (sus_neural)) %>%
mutate(CT = mCT,
hrCT = case_when(!is.na(hrCT) ~ hrCT,
TRUE ~ CT))
Warning: Detected an unexpected many-to-many relationship between `x` and `y`.
obs_neural$nlabels <- obs_neural$labels %>%
left_join(hr_long,
by = 'leiden3') %>%
filter(!leiden3 %in% (sus_neural)) %>%
mutate(CT = mCT,
hrCT = case_when(!is.na(hrCT) ~ hrCT,
TRUE ~ CT))
obs_neural$nobs %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = CT), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(CT) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = CT, color = CT)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$nobs %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = hrCT), pointsize = 0.8, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(hrCT) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = hrCT, color = hrCT)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$nobs %>%
ggplot(aes(x=umap1,y=umap2)) +
scattermore::geom_scattermore(aes(color = as.factor(hrCT)), pointsize = 2.1, alpha = 0.5) +
ggrepel::geom_label_repel(data = . %>% group_by(CT, hrCT) %>%
summarise(umap1 = median(umap1),
umap2 = median(umap2)),
aes(label = hrCT, color = hrCT)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(9), pals::kelly()) %>% unname()) +
cowplot::theme_cowplot() + theme(legend.position = "none") + facet_wrap(~CT)

hclust
pb <- pb <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.pseudoBulk.leiden3.csv.gz')
colnames(pb) <- gsub("\\.\\d+","",colnames(pb))
hvg <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hvg2000.csv.gz')[-1,]
rnames <- pb$V1
clust <- str_extract(rnames, '\\d+') %>% as.integer()
pb <- pb[,-1] %>% as.matrix()
row.names(pb) <- as.character(clust)
pb <- pb[as.character(obs_neural$nlabels$leiden3),]
pb_norm <- metamoRph::normalize_data(t(pb), sample_scale = 'cpm') %>% t()
Sample CPM scaling
log1p scaling
pb_norm <- pb_norm[,hvg$V2]
#pb_norm <- pb_norm[,hvg$V2[!hvg$V2 %in% c(cc_genes,ribo_genes)]]
# https://stats.stackexchange.com/questions/31565/compute-a-cosine-dissimilarity-matrix-in-r
sim <- pb_norm / sqrt(rowSums(pb_norm * pb_norm))
sim <- sim %*% t(sim)
D_sim <- as.dist(1 - sim)
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
hclust_sim <- hclust(D_sim, method = 'average')
hclust_sim$labels <- obs_neural$nlabels %>% pull(leiden3)
library(ggtree)
p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$nlabels, by = c("label" = "leiden3")) %>%
mutate(techRatio = round(techRatio, digits = 2))
p + layout_dendrogram() +
geom_tiplab(aes(label = paste(label, CT, studyCount, TotalCount, techRatio, sep = ' - '), color = CT)) +
theme_dendrogram(plot.margin=margin(16,16,300,16)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
guides(color="none")

p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$nlabels %>% mutate(studies = case_when(studyCount ==1 ~ studies,
TRUE ~ "multiple")), by = c("label" = "leiden3"))
p + layout_dendrogram() +
geom_tiplab(aes(label = paste(label, CT, studies, sep = ' - '), color = CT)) +
geom_tippoint(aes(shape = studies), size= 3) +
theme_dendrogram(plot.margin=margin(16,16,300,16)) +
scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
guides(color="none")

NA
NA
NA
NA
---
title: "Human Mature Eye, Neural Assessment"
output:
 html_notebook:
  author: "David McGaughey"
  date: "`r Sys.Date()`"
  theme: flatly
  toc: true
  toc_float: true
  code_folding: show
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  message = FALSE,  warning = FALSE,
  collapse = TRUE,
  fig.width = 12, fig.height = 8,
  comment = "#>",
  dpi=300
)
```

# Stage 4

## Biowulf2
```{bash, eval = FALSE}
cd /data/OGVFB_BG/scEiaD/2024_02_28/snakeout/hs111_mature_eye_full/neural_cells
mamba deactivate; mamba activate; bash ~/git/scEiaD_modeling/Snakemake.wrapper.sh ~/git/scEiaD_modeling/workflow/Snakefile ~/git/scEiaD_modeling/config/config_hs111_mature_eye_full__neural.yaml ~/git/scEiaD_modeling/config/cluster.json
```

## Assess Output
```{r}
library(tidyverse)
source('analysis_scripts.R')
obs_neural <- pull_obs('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.obs.csv.gz', machine_label = 'MCT_scANVI_step4', drop_col = FALSE)
diff_neural <- pull_diff("~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.difftesting.leiden3.csv.gz")
```
### Ratio (percentage) of labelled cell types for each leiden3 cluster
```{r}
obs_neural$labels %>% 
  arrange(mCT) %>% 
  mutate(leiden3 = as.factor(leiden3)) %>% 
  DT::datatable(filter = 'top')
```

### Mixed clusters
```{r}
obs_neural$labels %>% 
  filter(grepl(",", mMCT)) %>% 
  arrange(mCT) %>% 
  mutate(leiden3 = as.factor(leiden3)) %>% 
  DT::datatable(filter = 'top')
```

## UMAP Plots
```{r, fig.width=12, fig.height=12}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = MCT_scANVI_step4), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(MCT_scANVI_step4) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = MCT_scANVI_step4, color = MCT_scANVI_step4)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = MCT_scANVI), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none")


obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = studyRatio), pointsize = 0.7) +
  scale_color_viridis_c() +
  cowplot::theme_cowplot() 

obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = mMCT), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(mMCT) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = mMCT, color = mMCT)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none")


```

## hclust
Take pseudobulk values (at the cluster level) and hierarchically cluster them to ensure 
there aren't any issues in either the overall structure (e.g. rod and cones are intersperse)d
and/or to identify any potential mislabeled clusters

```{r, fig.width = 18, fig.height = 10}
pb <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.pseudoBulk.leiden3.csv.gz')
colnames(pb) <- gsub("\\.\\d+","",colnames(pb))
hvg <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hvg5000.csv.gz')[-1,]
rnames <- pb$V1
clust <- str_extract(rnames, '\\d+') %>% as.integer()
pb <- pb[,-1] %>% as.matrix()
row.names(pb) <- as.character(clust)
pb <- pb[as.character(obs_neural$labels$leiden3),]

pb_norm <- metamoRph::normalize_data(t(pb), sample_scale = 'cpm') %>% t() 

# remove cell cycle genes
conv_table <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db, 
                                    keys=gsub('\\.\\d+','',unique(colnames(pb_norm))),
                                    columns=c("ENSEMBL","SYMBOL", "MAP","GENENAME", "ENTREZID"), keytype="ENSEMBL")

cc_genes <- hvg %>% mutate(ENSEMBL = gsub("\\.\\d+","",V2)) %>% 
  left_join(conv_table, by = "ENSEMBL") %>% 
  mutate(cc_genes = case_when(SYMBOL %in% (Seurat::cc.genes.updated.2019 %>% unlist()) ~ TRUE)) %>% 
  filter(cc_genes) %>% pull(V2)
ribo_genes <- hvg %>% mutate(ENSEMBL = gsub("\\.\\d+","",V2)) %>% 
  left_join(conv_table, by = "ENSEMBL") %>% filter(grepl("^RPL|^RPS|^MT",SYMBOL)) %>% 
  pull(SYMBOL)

pb_norm <- pb_norm[,hvg$V2]
#pb_norm <- pb_norm[,hvg$V2[!hvg$V2 %in% c(cc_genes,ribo_genes)]]
# https://stats.stackexchange.com/questions/31565/compute-a-cosine-dissimilarity-matrix-in-r
sim <- pb_norm / sqrt(rowSums(pb_norm * pb_norm))
sim <- sim %*% t(sim)
D_sim <- as.dist(1 - sim)

hclust_sim <- hclust(D_sim, method = 'average')

hclust_sim$labels <- obs_neural$labels %>% pull(leiden3)

library(ggtree)
p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels, by = c("label" = "leiden3")) %>% 
  mutate(techRatio = round(techRatio, digits = ))
p + layout_dendrogram() +
  geom_tiplab(aes(label = paste(label, mMCT, studyCount, TotalCount, techRatio, sep = ' - '), color = mCT)) + 
  theme_dendrogram(plot.margin=margin(16,16,300,16)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  guides(color="none")


p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels %>% mutate(studies = case_when(studyCount ==1 ~ studies,
                                                                                TRUE ~ "multiple")), by = c("label" = "leiden3")) 

p + layout_dendrogram() +
  geom_tiplab(aes(label = paste(label, mMCT, studies, sep = ' - '), color = mCT)) + 
  geom_tippoint(aes(shape = studies), size= 3) +
  theme_dendrogram(plot.margin=margin(16,16,300,16)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  guides(color="none")



p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$labels %>% mutate(studies = case_when(studyRatio ==1 ~ studiesRatio,
                                                                                TRUE ~ "multiple")), by = c("label" = "leiden3")) 

p + layout_dendrogram() +
  geom_tiplab(aes(label = paste(label, mMCT, studies, sep = ' - '), color = mCT)) + 
  geom_tippoint(aes(shape = studies), size= 3) +
  theme_dendrogram(plot.margin=margin(16,16,300,16)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  guides(color="none")


```

## suspicious clusters

reasons:

- 1. study specific clusters in "wrong" (not with like) parts of the tree
- 2. mixed cell types

```{r}
sus_neural <- #c(71,93,126,112,144,39,21,85,135,7,74,36,124)
  c(
    # reason 1
    c(99, 97, 38, 50,40, 98, 94),
    # reason 2
    c(85, 95, 91, 52, 77)
  )
# to provide an additional layer of resolution to the cell type
hr_neural <- list()
```


### UMAP Plots
```{r, fig.width=16, fig.height=16}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = MCT_scANVI_step4), pointsize = 0.8, alpha = 0.5) +
  scattermore::geom_scattermore(data = obs_neural$obs %>% 
                                  left_join(obs_neural$labels, by = 'leiden3') %>% 
                                  filter(leiden3 %in% sus_neural),
                                color = 'red', pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              filter(leiden3 %in% sus_neural) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot()


obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural) %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = mCT), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              filter(leiden3 %in% sus_neural) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot()
```

# Call CT

```{r}
hr_neural <- list()
```

## photoreceptor

**Removing two more clusters with no ARR3 / RHO signal (and suspicious hclust) above**

https://www.nature.com/articles/s41598-020-66092-9
```{r}

tib <- diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT %in% c('rod','cone'), !base %in% sus_neural) %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('ARR3','OPN1LW','OPN1SW','RHO', 'OPN1MW', 'RCVRN',"CRX","PROM1")) %>% 
  select(SYMBOL, base, logfoldchanges) %>% 
  pivot_wider(values_from = logfoldchanges, names_from = base)

mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)

col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

sus_neural <- c(sus_neural,
                           c(86,29))

hr_neural$`cone (s)` <- c(87)
hr_neural$`cone (ml)` <- c(72,74,20,90,81)
```

```{r echo=FALSE, fig.cap="pr", out.width = '20%'}
knitr::include_graphics("images/pr.png")
# https://www.nature.com/articles/s41598-020-66092-9
```


```{r, fig.width=9, fig.height=9}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural,
         mCT %in% c("rod", "cone")) %>% 
  left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
             by = 'leiden3') %>% 
  mutate(`Cell Type` = case_when(is.na(`Cell Type`) ~ mCT,
                                 TRUE ~ `Cell Type`)) %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = 
                       c(pals::alphabet2(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) + 
  cowplot::theme_cowplot()
```


## bipolar

```{r echo=FALSE, fig.cap="bipolar", out.width = '20%'}
knitr::include_graphics("images/bipolar.png")
# https://www.nature.com/articles/s41598-020-66092-9
```



```{r}
tib <- diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT %in% c('bipolar','rod bipolar'), !base %in% sus_neural) %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('PRKCA','GRM6','GRIK1','NIF3L1',
                       'LINC00470','DOK5','NELL2','STX18',
                       'ODF2L','FAM19A4','MEIS2','CALB1', 'FUT4',
                       'SCG2','LRPPRC','FEZF1')) %>% 
  select(SYMBOL, base, logfoldchanges) %>% 
  pivot_wider(values_from = logfoldchanges, names_from = base)

mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)

col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

hr_neural$`bipolar (rod)` <- c(12,78,32)
hr_neural$`bipolar (off)` <- c(11,28,33,36,46,17)
hr_neural$`bipolar (on)` <- c(68,55,6,82,27,30,39)
```

```{r, fig.width=9, fig.height=9}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural,
         mCT %in% c("bipolar", "rod bipolar")) %>% 
  left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
             by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = 
                       c(pals::alphabet2(), pals::glasbey(),
                         pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) + 
  cowplot::theme_cowplot()
```

## horizontal

```{r echo=FALSE, fig.cap="bipolar", out.width = '20%'}
knitr::include_graphics("images/horizontal.png")
# https://www.nature.com/articles/s41598-020-66092-9
```

```{r}
diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT == 'horizontal') %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('ONECUT1','ONECUT2','LHX1','ISL1')) %>% 
  select(SYMBOL, base, logfoldchanges) %>% 
  ggplot(aes(x=as.factor(base), y=SYMBOL, color = logfoldchanges)) + 
  geom_point(size =10) +
  scale_color_viridis_c()


tib <- diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT == 'horizontal') %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('ONECUT1','ONECUT2','LHX1','ISL1')) %>% 
  select(SYMBOL, base, logfoldchanges) %>% 
  pivot_wider(values_from = logfoldchanges, names_from = base)

mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

hr_neural$`horizontal (h1)` <- c(18,70)
hr_neural$`horizontal (h2)` <- c(58)
```

```{r, fig.width=9, fig.height=9}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural,
         mCT %in% c("horizontal")) %>% 
  left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
             by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = 
                       c(pals::alphabet2(), pals::glasbey(),
                         pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) + 
  cowplot::theme_cowplot()
```


## amacrine
Most amacrine cells are inhibitory neurons utilizing GABA or glycine as neurotransmitters. By assessing the expression of marker genes for GABAergic (glutamate carboxylase, GAD1 and GAD2) and glycinergic (SLC6A9, encoding the high affinity glycine transporter GLYT1) amacrines20, we identified 16 putative GABAergic and 8 putative glycinergic amacrine cell types among a total of 25 types (Fig. 3a,b). One type (C14) expressed none of these three genes at high levels, and might correspond to a non-GABAergic non-Glycinergic (nGnG) type identified in mouse2122 . One of the glycinergic types (C17) also expressed GAD2, raising the possibility that it uses both transmitters.

**REMOVING CLUSTER 0 AS IT IS STUDY SPECIFIC AND NOT EXPRESSING ANY KNOWN GABA/GLYCI MARKERS**

```{r, fig.width=6, fig.height=2}


tib <- diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT == 'amacrine') %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('GAD1','GAD2','SLC6A9','NFIA')) %>% 
  select(SYMBOL, base, logfoldchanges) %>% 
  pivot_wider(values_from = logfoldchanges, names_from = base)

mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)
col_fun = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

sus_neural <- c(sus_neural,
                           c(0))
hr_neural$`amacrine (gabanergic)` <- c(76,67,56,59,37,62,73,15,69,44,75,83,24,66,22,26,80,41,61,91,42)
hr_neural$`amacrine (glycinergic)` <- c(25,16,88,43)
hr_neural$`amacrine (gaba/glyci)` <- c(91,31,65)
```

```{r, fig.width=9, fig.height=9}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural,
         mCT %in% c("amacrine")) %>% 
  left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
             by = 'leiden3') %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = 
                       c(pals::alphabet2(), pals::glasbey(),
                         pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) + 
  cowplot::theme_cowplot()
```


## retinal ganglion

These don't match up to the published markers very well - for example the parasol RG seem to be a mixed ON/OFF cluster. Should run a retinal ganglion specific scVI run and see whether that does a better job. 

```{r}

tib <- diff_neural$diff_testing %>% 
  left_join(obs_neural$labels, by = c('base'='leiden3')) %>% 
  filter(mCT %in% c('retinal ganglion'), !base %in% sus_neural) %>% 
  left_join(conv_table) %>% 
  filter(SYMBOL %in% c('TPBG','TBR1','FABP4','CHRNA2', 'LMO2',
  'EOMES','SSTR2','FOXP2','FOXP1','PRR35','CARTPT',
  'CDKN2A','ARPP21','OPN4', 'NEFM',
  'TUBB3')) %>% 
  select(SYMBOL, base, scores) %>% 
  pivot_wider(values_from = scores, names_from = base)

mat <- tib %>% select(-1) %>% as.matrix()
row.names(mat) <- tib %>% pull(1)

col_fun = circlize::colorRamp2(c(-15, 0, 15), c("blue", "white", "red"))
ComplexHeatmap::Heatmap(mat, col=col_fun)

```
```{r}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(mCT == 'retinal ganglion', SubCellType != '') %>% 
  left_join(obs_neural$labels) %>% 
  group_by(leiden3, SubCellType) %>% 
  summarise(Count = n()) %>% 
  mutate(Ratio = Count/sum(Count)) %>% 
  filter(Ratio > 0.5) %>% arrange(SubCellType)

hr_neural$`retinal ganglion (ON midget)` <- c(45, 54,63, 92)
hr_neural$`retinal ganglion (OFF midget)` <- c(19,34,57,71,79, 89, 96)

hr_neural$`retinal ganglion (parasol)` <- c(64)
```
```{r echo=FALSE, fig.cap="pr", out.width = '20%'}
knitr::include_graphics("images/rgc.png")
# https://www.nature.com/articles/s41598-020-66092-9
```


```{r, fig.width=9, fig.height=9}
obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  filter(!leiden3 %in% sus_neural,
         mCT %in% c("retinal ganglion")) %>% 
  left_join(hr_neural %>% enframe(name = 'Cell Type', value = 'leiden3') %>% unnest(leiden3),
             by = 'leiden3') %>% 
  mutate(`Cell Type` = case_when(is.na(`Cell Type`) ~ mCT,
                                 TRUE ~ `Cell Type`)) %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = `Cell Type`), pointsize = 0.8, alpha = 1) +
  ggrepel::geom_label_repel(data = . %>% group_by(leiden3) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = leiden3)) +
  scale_color_manual(values = 
                       c(pals::glasbey(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(n=12)) %>% unname()) + 
  cowplot::theme_cowplot()
```




# NEW 

Update overall graphics on the new labels

## UMAP
```{r, fig.width=12, fig.height=9}

hr_long <- hr_neural %>% enframe(name = 'hrCT', value = 'leiden3') %>% unnest(leiden3) 

obs_neural$nobs <- obs_neural$obs %>% 
  left_join(obs_neural$labels, by = 'leiden3') %>% 
  left_join(hr_long, 
            by = 'leiden3') %>%
  filter(!leiden3 %in% (sus_neural)) %>% 
  mutate(CT = mCT,
         hrCT = case_when(!is.na(hrCT) ~ hrCT,
                          TRUE ~ CT))

obs_neural$nlabels <- obs_neural$labels %>% 
  left_join(hr_long, 
            by = 'leiden3') %>%
  filter(!leiden3 %in% (sus_neural)) %>% 
  mutate(CT = mCT,
         hrCT = case_when(!is.na(hrCT) ~ hrCT,
                          TRUE ~ CT))

obs_neural$nobs %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = CT), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(CT) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = CT, color = CT)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$nobs %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = hrCT), pointsize = 0.8, alpha = 0.5) +
  ggrepel::geom_label_repel(data = . %>% group_by(hrCT) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = hrCT, color = hrCT)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none")

obs_neural$nobs %>% 
  ggplot(aes(x=umap1,y=umap2)) +
  scattermore::geom_scattermore(aes(color = as.factor(hrCT)), pointsize = 2.1, alpha = 0.5) +
    ggrepel::geom_label_repel(data = . %>% group_by(CT, hrCT) %>% 
                              summarise(umap1 = median(umap1),
                                        umap2 = median(umap2)),
                            aes(label = hrCT, color = hrCT)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey(), pals::alphabet(), pals::brewer.set1(9), pals::kelly()) %>% unname()) + 
  cowplot::theme_cowplot() + theme(legend.position = "none") + facet_wrap(~CT)
```

## hclust
```{r, fig.width = 18, fig.height = 10}
pb <- pb <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hs111_mature_eye_20240924_full__neural5000hvg_200e_50l.pseudoBulk.leiden3.csv.gz')
colnames(pb) <- gsub("\\.\\d+","",colnames(pb))
hvg <- data.table::fread('~/data/scEiaD_modeling/hs111_mature_eye_neural/hvg2000.csv.gz')[-1,]
rnames <- pb$V1
clust <- str_extract(rnames, '\\d+') %>% as.integer()
pb <- pb[,-1] %>% as.matrix()
row.names(pb) <- as.character(clust)
pb <- pb[as.character(obs_neural$nlabels$leiden3),]

pb_norm <- metamoRph::normalize_data(t(pb), sample_scale = 'cpm') %>% t()

pb_norm <- pb_norm[,hvg$V2]
#pb_norm <- pb_norm[,hvg$V2[!hvg$V2 %in% c(cc_genes,ribo_genes)]]
# https://stats.stackexchange.com/questions/31565/compute-a-cosine-dissimilarity-matrix-in-r
sim <- pb_norm / sqrt(rowSums(pb_norm * pb_norm))
sim <- sim %*% t(sim)
D_sim <- as.dist(1 - sim)

hclust_sim <- hclust(D_sim, method = 'average')

hclust_sim$labels <- obs_neural$nlabels %>% pull(leiden3)

library(ggtree)
p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$nlabels, by = c("label" = "leiden3")) %>%
  mutate(techRatio = round(techRatio, digits = 2))
p + layout_dendrogram() +
  geom_tiplab(aes(label = paste(label, CT, studyCount, TotalCount, techRatio, sep = ' - '), color = CT)) +
  theme_dendrogram(plot.margin=margin(16,16,300,16)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
  guides(color="none")


p <- ggtree(hclust_sim)
p$data <- p$data %>% left_join(obs_neural$nlabels %>% mutate(studies = case_when(studyCount ==1 ~ studies,
                                                                                    TRUE ~ "multiple")), by = c("label" = "leiden3"))

p + layout_dendrogram() +
  geom_tiplab(aes(label = paste(label, CT, studies, sep = ' - '), color = CT)) +
  geom_tippoint(aes(shape = studies), size= 3) +
  theme_dendrogram(plot.margin=margin(16,16,300,16)) +
  scale_color_manual(values = c(pals::alphabet2(), pals::glasbey()) %>% unname()) +
  guides(color="none")




```

# Output
```{r}
save(obs_neural, file = 'Human_Mature_Eye_full__stage4_neural.obs.freeze20241107.Rdata')
obs_neural$nobs %>% select(barcode, leiden3, CT, hrCT) %>% write_csv('Human_Mature_Eye_full__stage4_neural.CTcalls.freeze20241107.csv.gz')
```
